Professor David Eyre
Professor of Infectious Diseases
- Robertson Fellow
- Infectious Diseases Clinician
My research interests include the use of whole-genome sequencing as a tool for understanding the epidemiology and transmission of bacterial and fungal pathogens. My previous work has described the transmission of the major healthcare-associated pathogen Clostridium difficile and has also included large-scale sequencing projects tracking the spread of gonorrhoea and the emerging multi-drug resistant fungus Candida auris. I am currently working on developing mathematical models for pathogen transmission that allow risk factors for transmission to be identified, as a means to suggest potential interventions to prevent infections spreading.
I am also interested in using sequencing technologies as a novel tool for culture-independent microbiology diagnostics. These technologies offer the prospect of same-day diagnosis of infection, rather than having to wait several days for bacteria to grow in the lab. I have developed methods using sequencing data to detect the presence of infection, e.g. from orthopedic devices removed from patients, as well as predict antibiotic resistance, e.g. in Enterobacteriaceae and Neisseria gonorrhoeae.
Additionally I work on using routinely collected healthcare data to investigate the epidemiology of infectious diseases and to investigate individual patient responses to infection and treatment.
I work closely with the Modernising Medical Microbiology consortium on several of these projects.
Integrating at-scale health data into doctoral training (Preprint)
Nichols TE. et al, (2023)
Scalable federated learning for emergency care using low cost microcomputing: Real-world, privacy preserving development and evaluation of a COVID-19 screening test in UK hospitals
Soltan AAS. et al, (2023)
Protection against SARS-CoV-2 Omicron BA.4/5 variant following booster vaccination or breakthrough infection in the UK.
Wei J. et al, (2023), Nature communications, 14
Comparing genomic variant identification protocols for Candida auris.
Li X. et al, (2023), Microb Genom, 9
An adversarial training framework for mitigating algorithmic biases in clinical machine learning
Yang J. et al, (2023), npj Digital Medicine, 6